A consensus-based ensemble approach to improve transcriptome assembly

De novo assembly and annotation of the transcriptome of the endangered seagrass Zostera capensis: Insights from differential gene expression under thermal stress

Seagrasses are important marine ecosystem engineers but anthropogenic impacts and climate change have led to numerous population declines globally. In South Africa, Zostera capensis is endangered due to fragmented populations and heavy anthropogenic pressures on estuarine ecosystems that house the core of the populations. Addressing questions of how pressures such as climate change affect foundational species, including Z. capensis are crucial to supporting their conservation and underpin restoration efforts. Here we use ecological transcriptomics to study key functional responses of Z. capensis through quantification of gene expression after thermal stress and present the first reference transcriptome of Z. capensis. Four de novo reference assemblies (Trinity, IDBA-tran, RNAspades, SOAPdenovo) filtered through the EvidentialGene pipeline resulted in 153,755 transcripts with a BUSCO score of 66.1% for completeness. Differential expression analysis between heat stressed (32 °C for three days) and pre-warming plants identified genes involved in photosynthesis, oxidative stress, translation, metabolic and biosynthetic processes in the Z. capensis thermal stress response. This reference transcriptome is a significant contribution to the limited available genomic resources for Z. capensis and represents a vital tool for addressing questions around the species restoration and potential functional responses to warming marine environments.

Normalized Workflow to Optimize Hybrid De Novo Transcriptome Assembly for Non-Model Species: A Case Study in Lilium ledebourii (Baker) Boiss

A high-quality transcriptome is required to advance numerous bioinformatics workflows. Nevertheless, the effectuality of tools for de novo assembly and real precision assembled transcriptomes looks somewhat unexplored, particularly for non-model organisms with complicated (very long, heterozygous, polyploid) genomes. To disclose the performance of various transcriptome assembly programs, this study built 11 single assemblies and analyzed their performance on some significant reference-free and reference-based criteria. As well as to reconfirm the outputs of benchmarks, 55 BLAST were performed and compared using 11 constructed transcriptomes. Concisely, normalized benchmarking demonstrated that Velvet–Oases suffer from the worst results, while the EvidentialGene strategy can provide the most comprehensive and accurate transcriptome of Lilium ledebourii (Baker) Boiss. The BLAST results also confirmed the superiority of EvidentialGene, so it could capture even up to 59% more (than Velvet–Oases) unique gene hits. To promote assembly optimization, with the help of normalized benchmarking, PCA and AHC, it is emphasized that each metric can only provide part of the transcriptome status, and one should never settle for just a few evaluation criteria. This study supplies a framework for benchmarking and optimizing the efficiency of assembly approaches to analyze RNA-Seq data and reveals that selecting an inefficient assembly strategy might result in less identification of unique gene hits.